Method and system for efficient channel estimation

ABSTRACT

Aspects of the present disclosure describe an efficient channel estimation algorithm for high-speed processing of dedicated reference signals. The channel estimation algorithm may utilize one or more compressed interpolation matrices. The compressed interpolation matrices may be selected based on the Doppler value and signal to noise ratio (SNR) of the channel.

CLAIM OF PRIORITY UNDER 35 U.S.C. §119

The present Application for Patent claims priority to U.S. ProvisionalApplication No. 61/265,317, entitled, “Method and System for DedicatedReference Signal (DRS) Channel Estimation,” filed Nov. 30, 2009,assigned to the assignee hereof and expressly incorporated herein byreference.

TECHNICAL FIELD

Certain aspects of the present disclosure generally relate to wirelesscommunications and, more particularly, to an efficient channelestimation algorithm utilizing compressed interpolation matrices.

BACKGROUND

The third Generation Partnership Project (3GPP) Long Term Evolution(LTE) represents a major advance in cellular technology and is the nextstep forward in cellular 3G services as a natural evolution of GlobalSystem for Mobile Communications (GSM) and Universal MobileTelecommunications System (UMTS). LTE provides for an uplink speed of upto 50 megabits per second (Mbps) and a downlink speed of up to 100 Mbpsand brings many technical benefits to cellular networks. LTE is designedto meet carrier needs for high-speed data and media transport as well ashigh-capacity voice support well into this decade. Bandwidth is scalablefrom 1.25 MHz to 20 MHz. This suits the needs of different networkoperators that have different bandwidth allocations, and also allowsoperators to provide different services based on spectrum. LTE is alsoexpected to improve spectral efficiency in 3G networks, allowingcarriers to provide more data and voice services over a given bandwidth.LTE encompasses high-speed data, multimedia unicast and multimediabroadcast services.

Physical layer (PHY) of LTE standard is a highly efficient means ofconveying both data and control information between an enhanced basestation (eNodeB) and mobile user equipment (UE). LTE PHY employsadvanced technologies that are new to cellular applications. Theseinclude Orthogonal Frequency Division Multiplexing (OFDM) and MultipleInput Multiple Output (MIMO) data transmission. In addition, LTE PHYuses Orthogonal Frequency Division Multiple Access (OFDMA) on thedownlink (DL) and Single Carrier—Frequency Division Multiple Access(SC-FDMA) on the uplink (UL). OFDMA allows data to be directed to orfrom multiple users on a subcarrier-by-subcarrier basis for a specifiednumber of symbol periods.

LTE-Advanced is an evolving mobile communication standard for providing4G services. Being defined as 3G technology, LTE does not meet therequirements for 4G also called International MobileTelecommunications-Advanced (IMT-Advanced) as defined by theInternational Telecommunication Union such as peak data rates up to 1Gbit/s. Besides the peak data rate, LTE-Advanced also targets fasterswitching between power states and improved performance at the celledge.

SUMMARY

Certain aspects of the present disclosure provide a method for wirelesscommunications. The method generally includes determining a first signalto noise ratio (SNR) value for a received signal, selecting, based onthe first SNR value, a first matrix from one or more compressedinterpolation matrices, wherein each of the compressed interpolationmatrices corresponds to a subset of symbols in a subframe, and obtaininga first channel estimation matrix utilizing the received signal and thefirst matrix.

Certain aspects of the present disclosure provide an apparatus forwireless communications. The apparatus generally includes means fordetermining a first signal to noise ratio (SNR) value for a receivedsignal, means for selecting, based on the first SNR value, a firstmatrix from one or more compressed interpolation matrices, wherein eachof the compressed interpolation matrices corresponds to a subset ofsymbols in a subframe, and means for obtaining a first channelestimation matrix utilizing the received signal and the first matrix.

Certain aspects of the present disclosure provide an apparatus forwireless communications. The apparatus generally includes logic fordetermining a first signal to noise ratio (SNR) value for a receivedsignal, logic for selecting, based on the first SNR value, a firstmatrix from one or more compressed interpolation matrices, wherein eachof the compressed interpolation matrices corresponds to a subset ofsymbols in a subframe, and logic for obtaining a first channelestimation matrix utilizing the received signal and the first matrix.

Certain aspects of the present disclosure provide an apparatus forwireless communications. The apparatus generally includes at least oneprocessor configured to determine a first signal to noise ratio (SNR)value for a received signal, select, based on the first SNR value, afirst matrix from one or more compressed interpolation matrices, whereineach of the compressed interpolation matrices corresponds to a subset ofsymbols in a subframe, and obtain a first channel estimation matrixutilizing the received signal and the first matrix, and a memory coupledto the at least one processor.

Certain aspects provide a computer-program product for wirelesscommunications, comprising a computer-readable medium havinginstructions stored thereon, the instructions being executable by one ormore processors. The instructions generally include instructions fordetermining a first signal to noise ratio (SNR) value for a receivedsignal, instructions for selecting, based on the first SNR value, afirst matrix from one or more compressed interpolation matrices, whereineach of the compressed interpolation matrices corresponds to a subset ofsymbols in a subframe, and instructions for obtaining a first channelestimation matrix utilizing the received signal and the first matrix.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a diagram of a wireless communications network, inaccordance with certain aspects of the present disclosure.

FIG. 2 illustrates a block diagram of an example access point and userterminals, in accordance with certain aspects of the present disclosure.

FIG. 3 illustrates a block diagram of an example wireless device, inaccordance with certain aspects of the present disclosure.

FIG. 4 illustrates an example grid structure comprising DedicatedReference Signals (DRSs), in accordance with certain aspects of thepresent disclosure.

FIG. 5 illustrates an example grid structure corresponding to a factorfour compressed 2D-MMSE interpolation matrix, in accordance with certainaspects of the present disclosure.

FIG. 6 illustrates a two-stage SNR and channel estimation algorithm, inaccordance with certain aspects of the present disclosure.

FIG. 7 illustrates a variation of the two-stage SNR and channelestimation algorithm, in accordance with certain aspects of the presentdisclosure.

FIG. 8 illustrates throughput of a system utilizing the two-stagechannel estimation algorithm, in accordance with certain aspects of thepresent disclosure.

FIG. 9 illustrates example operations for a channel estimationalgorithm, in accordance with certain aspects of the present disclosure.

FIG. 9A illustrates example components capable of performing theoperations shown in FIG. 9.

DETAILED DESCRIPTION

Various aspects are now described with reference to the drawings. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth in order to provide a thorough understanding ofone or more aspects. It may be evident; however, that such aspect(s) maybe practiced without these specific details.

As used in this application, the terms “component,” “module,” “system”and the like are intended to include a computer-related entity, such asbut not limited to hardware, firmware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a programand/or a computer. By way of illustration, both an application runningon a computing device and the computing device can be a component. Oneor more components can reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components may communicate by way oflocal and/or remote processes such as in accordance with a signal havingone or more data packets, such as data from one component interactingwith another component in a local system, distributed system, and/oracross a network such as the Internet with other systems by way of thesignal.

Furthermore, various aspects are described herein in connection with aterminal, which can be a wired terminal or a wireless terminal. Aterminal can also be called a system, device, subscriber unit,subscriber station, mobile station, mobile, mobile device, remotestation, remote terminal, access terminal, user terminal, terminal,communication device, user agent, user device, or user equipment (UE). Awireless terminal may be a cellular telephone, a satellite phone, acordless telephone, a Session Initiation Protocol (SIP) phone, awireless local loop (WLL) station, a personal digital assistant (PDA), ahandheld device having wireless connection capability, a computingdevice, or other processing devices connected to a wireless modem.Moreover, various aspects are described herein in connection with a basestation. A base station may be utilized for communicating with wirelessterminal(s) and may also be referred to as an access point, a Node B, orsome other terminology.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

The techniques described herein may be used for various wirelesscommunication networks such as Code Division Multiple Access (CDMA)networks, Time Division Multiple Access (TDMA) networks, FrequencyDivision Multiple Access (FDMA) networks, Orthogonal FDMA (OFDMA)networks, Single-Carrier FDMA (SC-FDMA) networks, etc. The terms“networks” and “systems” are often used interchangeably. A CDMA networkmay implement a radio technology such as Universal Terrestrial RadioAccess (UTRA), CDMA 2000, etc. UTRA includes Wideband-CDMA (W-CDMA).CDMA2000 covers IS-2000, IS-95 and IS-856 standards. A TDMA network mayimplement a radio technology such as Global System for MobileCommunications (GSM).

An OFDMA network may implement a radio technology such as Evolved UTRA(E-UTRA), IEEE 802.11, IEEE 802.16, IEEE 802.20, Flash-OFDM®, etc. UTRA,E-UTRA, and GSM are part of Universal Mobile Telecommunication System(UMTS). Long Term Evolution (LTE) is a recent release of UMTS that usesE-UTRA. UTRA, E-UTRA, GSM, UMTS and LTE are described in documents froman organization named “3rd Generation Partnership Project” (3GPP).CDMA2000 is described in documents from an organization named “3rdGeneration Partnership Project 2” (3GPP2). These various radiotechnologies and standards are known in the art. For clarity, certainaspects of the techniques are described below for LTE, and LTEterminology is used in much of the description below. It should be notedthat LTE terminology is used by way of illustration and the scope of thedisclosure is not limited to LTE.

Single carrier frequency division multiple access (SC-FDMA), whichutilizes single carrier modulation and frequency domain equalization hassimilar performance and essentially the same overall complexity as thoseof an OFDMA system. SC-FDMA signal may have lower peak-to-average powerratio (PAPR) because of its inherent single carrier structure. SC-FDMAmay be used in the uplink communications where lower PAPR greatlybenefits the mobile terminal in terms of transmit power efficiency. Itis currently a working assumption for uplink multiple access scheme in3GPP Long Term Evolution (LTE), or Evolved UTRA.

An Example MIMO System

FIG. 1 illustrates a multiple-access MIMO system 100 with access pointsand user terminals. For simplicity, only one access point 110 is shownin FIG. 1. An access point (AP) may be a fixed station that communicateswith the user terminals and may also be referred to as a base station orsome other terminology. A user terminal may be fixed or mobile and mayalso be referred to as a mobile station, a station (STA), a client, awireless device, or some other terminology. A user terminal may be awireless device, such as a cellular phone, a personal digital assistant(PDA), a handheld device, a wireless modem, a laptop computer, apersonal computer, etc.

Access point 110 may communicate with one or more user terminals 120 atany given moment on the downlink and uplink. The downlink (i.e., forwardlink) is the communication link from the access point to the userterminals, and the uplink (i.e., reverse link) is the communication linkfrom the user terminals to the access point. A user terminal may alsocommunicate peer-to-peer with another user terminal. A system controller130 couples to and provides coordination and control for the accesspoints.

System 100 employs multiple transmit and multiple receive antennas fordata transmission on the downlink and uplink. Access point 110 isequipped with a number N_(ap) of antennas and represents themultiple-input (MI) for downlink transmissions and the multiple-output(MO) for uplink transmissions. A set N_(u) of selected user terminals120 collectively represents the multiple-output for downlinktransmissions and the multiple-input for uplink transmissions. Incertain cases, it may be desirable to have N_(ap)≧N_(u)≧1 if the datasymbol streams for the N_(u) user terminals are not multiplexed in code,frequency or time by some means. N_(u) may be greater than N_(ap) if thedata symbol streams can be multiplexed using different code channelswith CDMA, disjoint sets of sub-bands with OFDM, and so on. Eachselected user terminal transmits user-specific data to and/or receivesuser-specific data from the access point. In general, each selected userterminal may be equipped with one or multiple antennas (i.e., N_(ut)≧1).The N_(u) selected user terminals can have the same or different numberof antennas.

MIMO system 100 may be a time division duplex (TDD) system or afrequency division duplex (FDD) system. For a TDD system, the downlinkand uplink share the same frequency band. For an FDD system, thedownlink and uplink use different frequency bands. MIMO system 100 mayalso utilize a single carrier or multiple carriers for transmission.Each user terminal may be equipped with a single antenna (e.g., in orderto keep costs down) or multiple antennas (e.g., where the additionalcost can be supported). Each of the user terminals or the access pointmay estimate communications channels using dedicated reference signals.

FIG. 2 shows a block diagram 200 of access point 110 and two userterminals 120 m and 120 x in MIMO system 100. Access point 110 isequipped with N_(ap) antennas 224 a through 224 ap. User terminal 120 mis equipped with N_(ut,m) antennas 252 ma through 252 mu, and userterminal 120 x is equipped with N_(ut,x), antennas 252 xa through 252xu. Access point 110 is a transmitting entity for the downlink and areceiving entity for the uplink. Each user terminal 120 is atransmitting entity for the uplink and a receiving entity for thedownlink. As used herein, a “transmitting entity” is an independentlyoperated apparatus or device capable of transmitting data via afrequency channel, and a “receiving entity” is an independently operatedapparatus or device capable of receiving data via a frequency channel.In the following description, the subscript “dn” denotes the downlink,the subscript “up” denotes the uplink, N_(up) user terminals areselected for simultaneous transmission on the uplink, N_(A) userterminals are selected for simultaneous reception on the downlink,N_(up) may or may not be equal to N_(dn), and N_(up) and N_(dn) may bestatic values or can change for each scheduling interval. Beam-steeringor some other spatial processing technique may be used at the accesspoint and user terminal.

On the uplink, at each user terminal 120 selected for uplinktransmission, for example, user terminal 120 m, a TX data processor 288receives traffic data from a data source 286 and control data from acontroller 280. The controller 280 may be coupled with a memory 282. TXdata processor 288 processes (e.g., encodes, interleaves, and modulates)the traffic data {d_(up,m)} for the user terminal based on the codingand modulation schemes associated with the rate selected for the userterminal and provides a data symbol stream {s_(up,m)}. A TX spatialprocessor 290 performs spatial processing on the data symbol stream{s_(up,m)} and provides N_(ut,m) transmit symbol streams for theN_(ut,m) antennas. Each transmitter unit (TMTR) 254 receives andprocesses (e.g., converts to analog, amplifies, filters, and frequencyupconverts) a respective transmit symbol stream to generate an uplinksignal. N_(ut,m) transmitter units 254 provide N_(ut,m) uplink signalsfor transmission from N_(ut,m) antennas 252 to the access point 110.

A number N_(up) of user terminals may be scheduled for simultaneoustransmission on the uplink. Each of these user terminals performsspatial processing on its data symbol stream and transmits its set oftransmit symbol streams on the uplink to the access point.

At access point 110, N_(ap) antennas 224 a through 224 ap receive theuplink signals from all N_(up) user terminals transmitting on theuplink. Each antenna 224 provides a received signal to a respectivereceiver unit (RCVR) 222. Each receiver unit 222 performs processingcomplementary to that performed by transmitter unit 254 and provides areceived symbol stream.

A channel estimator 228 may perform channel estimation on the receivedsignals. The channel estimator 228 may utilize a channel estimationalgorithm utilizing compressed interpolation matrices, as described infurther detail in the rest of the document. An RX spatial processor 240performs receiver spatial processing on the N_(ap) received symbolstreams from N_(ap) receiver units 222 and provides N_(up) recovereduplink data symbol streams. The receiver spatial processing is performedin accordance with the channel correlation matrix inversion (CCMI),minimum mean square error (MMSE), successive interference cancellation(SIC), or some other technique. Each recovered uplink data symbol stream{s_(up,m)} is an estimate of a data symbol stream {s_(up,m)} transmittedby a respective user terminal. An RX data processor 242 processes (e.g.,demodulates, deinterleaves, and decodes) each recovered uplink datasymbol stream {s_(up,m)} in accordance with the rate used for thatstream to obtain decoded data. The decoded data for each user terminalmay be provided to a data sink 244 for storage and/or a controller 230for further processing. The controller 230 may be coupled with a memory232.

On the downlink, at access point 110, a TX data processor 210 receivestraffic data from a data source 208 for N_(dn) user terminals scheduledfor downlink transmission, control data from a controller 230, andpossibly other data from a scheduler 234. The various types of data maybe sent on different transport channels. TX data processor 210 processes(e.g., encodes, interleaves, and modulates) the traffic data for eachuser terminal based on the rate selected for that user terminal TX dataprocessor 210 provides N_(dn) downlink data symbol streams for theN_(dn) user terminals. A TX spatial processor 220 performs spatialprocessing on the N_(dn) downlink data symbol streams, and providesN_(ap) transmit symbol streams for the N_(ap) antennas. Each transmitterunit (TMTR) 222 receives and processes a respective transmit symbolstream to generate a downlink signal. N_(ap) transmitter units 222provide N_(ap) downlink signals for transmission from N_(ap) antennas224 to the user terminals.

At each user terminal 120, for example, user terminal 120 m, N_(ut,m)antennas 252 receive the N_(ap) downlink signals from access point 110.Each receiver unit (RCVR) 254 processes a received signal from anassociated antenna 252 and provides a received symbol stream. A channelestimator 278 may perform channel estimation on the received signals.The channel estimator 278 may utilize a channel estimation algorithmutilizing compressed interpolation matrices, as described in furtherdetail in the rest of the document. An RX spatial processor 260 performsreceiver spatial processing on N_(ut,m) received symbol streams fromN_(ut,m) receiver units 254 and provides a recovered downlink datasymbol stream {s_(dn,m)} for the user terminal. The receiver spatialprocessing is performed in accordance with the channel correlationmatrix inversion (CCMI), minimum mean square error (MMSE), or some othertechnique. An RX data processor 270 processes (e.g., demodulates,deinterleaves, and decodes) the recovered downlink data symbol stream toobtain decoded data for the user terminal. The decoded data for eachuser terminal may be provided to a data sink 272 for storage and/or acontroller 280 for further processing.

FIG. 3 illustrates various components that may be utilized in a wirelessdevice 302 that may be employed within the system 100. The wirelessdevice 302 is an example of a device that may be configured to implementthe various methods described herein. The wireless device 302 may be anaccess point 110 or a user terminal 120.

The wireless device 302 may include a processor 304 which controlsoperation of the wireless device 302. The processor 304 may also bereferred to as a central processing unit (CPU). Memory 306, which mayinclude both read-only memory (ROM) and random access memory (RAM),provides instructions and data to the processor 304. A portion of thememory 306 may also include non-volatile random access memory (NVRAM).The processor 304 typically performs logical and arithmetic operationsbased on program instructions stored within the memory 306. Theinstructions in the memory 306 may be executable to implement themethods described herein.

The wireless device 302 may also include a housing 308 that may includea transmitter 310 and a receiver 312 to allow transmission and receptionof data between the wireless device 302 and a remote location. Thetransmitter 310 and receiver 312 may be combined into a transceiver 314.A plurality of transmit antennas 316 may be attached to the housing 308and electrically coupled to the transceiver 314. The wireless device 302may also include (not shown) multiple transmitters, multiple receivers,and multiple transceivers.

The wireless device 302 may also include a signal detector 318 that maybe used in an effort to detect and quantify the level of signalsreceived by the transceiver 314. The signal detector 318 may detect suchsignals as total energy, energy per subcarrier per symbol, powerspectral density and other signals. The wireless device 302 may alsoinclude a digital signal processor (DSP) 320 for use in processingsignals.

The various components of the wireless device 302 may be coupledtogether by a bus system 322, which may include a power bus, a controlsignal bus, and a status signal bus in addition to a data bus.

Certain aspects of the present disclosure describe an efficient channelestimation algorithm for high-speed processing of dedicated referencesignals. The channel estimation algorithm may utilize one or morecompressed interpolation matrices. The compressed interpolation matricesmay be selected based on the Doppler value and SNR of the channel.

Referring back to the FIG. 3, the wireless device 302 may include asignal to noise ratio determining component 301 for determining signalto noise ratio of a received signal, an interpolation matrix selectingcomponent 303 for selecting a compressed interpolation matrix, and achannel estimating component 305 for estimating a channel using thereceived signal and the compressed interpolation matrix.

FIG. 4 illustrates an example grid structure 400 that may includesymbols utilized for transmission of Dedicated Reference Signals (DRS).The grid structure may comprise a Physical Resource Block (PRB) over asubframe. For example, the PRB may include 12 subcarriers and thesubframe may include 14 OFDM symbols. The grid structure illustratescode division multiplexing (spreading) in time domain. As shown, theOFDM symbols may comprise Time Division Multiplexing (TDM) controlsymbols 402, cell-specific or Common Reference Signals (CRSs) 404, DRSsignals 406 and data 408. As shown, the DRS signals 406 may betransmitted over a few symbols in the grid with a predetermined pattern.The DRS signals may be used by the receiver to estimate the channel.

In general, a receiver may perform per-grid channel estimation or noise(e.g., N_(t)) estimation using a channel estimation algorithm such as atwo dimensional minimum mean square error (2D-MMSE) algorithm. In the2D-MMSE algorithm, a channel estimation for all tones within a grid (H_(hat)) may be calculated as follows:H _(hat) =w·y _(P)in which w comprises an interpolator matrix with size N_(grid)×N_(P),wherein N_(grid) represents size of the grid. In the example illustratedin FIG. 4, grid size is equal to 12×14=168. In addition, N_(P)represents number of DRS tones per grid, and wherein y _(P) represents aDRS received signal of size N_(P)×1. It is noted that the interpolationmatrix w may be function of the estimated Path Delay Profile (PDP), theestimated Doppler, or reference signal patterns.

Certain aspects of the present disclosure include utilizing reduced sizeinterpolation matrices in a channel estimation algorithm such as2D-MMSE. Therefore, complexity of the channel estimation algorithm andthe required storage may be reduced. The reason for reduced complexitymay be multiplying smaller matrices compared to a system utilizingfull-size interpolation matrices.

For certain aspects, instead of utilizing a full size interpolationmatrix, a compressed interpolation matrix may be used in a channelestimation algorithm (e.g., 2D-MMSE) with minimal performancedegradation. For example, size of the full-size matrix may be equal to168×12 and size of the compressed interpolation matrix may be equal to48×12 (e.g., a compression factor of 4). Generally, the full-sizeinterpolation matrix may be calculated on the fly. A compressedinterpolation matrix may also be calculated on the fly, or it may becalculated in advance and stored in a memory inside a device.

For certain aspects, a plurality of compressed interpolation matricesmay be stored in the memory, corresponding to K different Doppler values(or different ranges of Doppler values) and S different SNR values. Forexample, for K=4 and S=5, twenty compressed interpolation matrices maybe defined. The Doppler steps may include a low Doppler range comprisingspeeds less than 30 kmh; first middle Doppler range comprising speedsgreater than or equal to 30 kmh and less than or equal to 60 kmh; secondmiddle Doppler range comprising speeds greater than 60 kmh and less thanor equal to 120 kmh; and a high Doppler range comprising speeds greaterthan 120 kmh. Also, five different SNR steps may be considered, such as5 dB, 10 dB, 15 dB, 20 dB, and 25 dB.

In the above example, the memory required to store the compressedinterpolation matrices may be calculated as follows:K×S×R×N _(DRS) ×N _(bits)=4×5×48×12×32=360 Kbitsin which N_(DRS) represents number of dedicated reference signals, and Rrepresents number of rows of the compressed interpolation matrix.

FIG. 5 illustrates an example grid structure 500 corresponding to afactor four compressed 2D-MMSE interpolator, in accordance with certainaspects of the present disclosure. As illustrated, the grid structuremay include 14 OFDM symbols in a subframe over 12 subcarriers. Channelvector ĥ_(i), i=0, . . . , 13 may be a 12×1 vector corresponding tochannel estimations for the i^(th) OFDM symbol for different subcarriers(e.g., 12 subcarriers). Information corresponding to a subset of symbols502 may be included in a compressed interpolator matrix w _(fact4). Forexample, the compressed interpolator matrix w _(fact4) may compriseinformation about a subset of OFDM symbols (e.g., the symbolscorresponding to channels ĥ₀, ĥ₄, ĥ₈ and ĥ₁₂). The channels ĥ₀, ĥ₄, ĥ₈and ĥ₁₂ may be estimated based on w_(fact4) and y_(p) utilizing achannel estimation algorithm (e.g., 2D-MMSE) as follows:

$\begin{bmatrix}{\hat{h}}_{0} \\{\hat{h}}_{4} \\{\hat{h}}_{8} \\{\hat{h}}_{12}\end{bmatrix} = {{\underset{\underset{\_}{\_}}{w}}_{{fact}\; 4} \cdot y_{p}}$

Channel values corresponding to other symbols in the subframe may bederived by interpolation. For example, h₁, h₂ and h₃ may be calculatedas follows:ĥ ₁=(3ĥ ₀ +ĥ ₄)/4ĥ ₂=(2ĥ ₀+2ĥ ₄)/4ĥ ₃=(ĥ ₀+3ĥ ₄)/4

Channel estimation algorithms such as MMSE may need an estimation ofsignal to noise ratio (SNR), for example, to calculate a correlationmatrix. The SNR may be dependent on Doppler speed and/or type of thechannel (e.g., Single User (SU)-MIMO Rank2, SU-MIMO Rank1, Multiuser(MU)-MIMO). For example, for SU-MIMO Rank2, differential in time ofde-spreaded signals may be used. In addition, noise variance σ² may becalculated based on the following equation:σ²=0.5E{|y _(p)(k,n ₁)−y _(p)(k,n _(i+1))|²}wherein k may represent frequency index, n may represent time index andE(x) may be the expected value of x.

In another example, for SU-MIMO Rank1 or MU-MIMO, the noise variance maybe estimated by calculating power of a co-channel de-spreaded signal.For certain aspects, SNR may be calculated for each PRB and averagedover a plurality of PRBs.

For certain aspects, noise (N_(t)) may also be estimated based onchannel estimation values, as follows:N _(t) =E{|r _(p)(k,n)−ĥ_(p)(k,n)|²}in which r (k,n) may be received DRS signal and ĥ_(p)(k,n) may bespreaded channel estimation value. The estimated channel may containsome bias. However, the performance loss may be negligible even if thebias is not removed.

For certain aspects of the present disclosure, SNR may be estimatedutilizing different methods. For lower speeds (e.g., speeds less than 60kmh), SNR may be estimated by differentiating DRS signals in timedomain. At mid to high speeds (e.g., speeds greater than or equal to 60kmh), estimation of SNR based only on differentiating DRS signals intime may be unreliable. Therefore, for speeds greater than or equal to60 kmh, six exemplary approaches for SNR estimation are explained infurther detail below.

For certain aspects, SNR may be estimated from common reference signals(CRS). For example, a receiver may utilize power delay profile (PDP)and/or Channel Quality Indicator (CQI) feedback for SNR estimation.However, the SNR estimation utilizing the PDP may be inaccurate due tonarrow band interference from other cells. Also, the CQI feedback maynot include knowledge of the SNR.

For certain aspects, mapping of the current Modulation Coding Scheme(MCS) maybe used to estimate SNR. The MCS may be optimized for amatching MCS table. However, one potential drawback of this scheme isthat an evolved Node B (eNB) may decide which MCS is to be used that maybe different from an MCS suggested by the UE.

For certain aspects, a predefined, fixed value (e.g., 20 dB) may be usedinstead of an estimated SNR. In another aspect, a measured SNR value maybe compared to a predefined threshold value. Based on the comparison, ifthe measured SNR is smaller than the predefined threshold, the SNR maybe set to a predefined low value (e.g., 10 dB). If the measured SNR isgreater than or equal to the predefined threshold, the SNR may be set toa predefined high value (e.g., 20 dB). The measured SNR may becalculated by differentiating DRS signals in time domain.

FIG. 6 shows diagram 600 illustrating a two-stage SNR and channelestimation algorithm, in accordance with certain aspects of the presentdisclosure. As illustrated, at first stage, a raw SNR value may beestimated using time domain signal differential approach utilizing(y₁-y₂) based SNR estimator 602, in which y₁ and y₂ are two consecutivereceived samples. The raw SNR value may then be compared to a thresholdusing block 604 to generate a coarse SNR estimate. If the raw SNR issmaller than the threshold, the coarse SNR estimate may be set to afirst value (e.g., 10 dB). If the raw SNR is greater than or equal tothe threshold, the coarse SNR estimate may be set to a second value(e.g., 20 dB).

The SNR estimate may be used to select a 2D-MMSE interpolator matrixwhich may be utilized to obtain the first stage channel estimation valueutilizing the block first stage 2D-MMSE 606. The block first stage2D-MMSE may utilize the preprocessed signal and the estimated SNR togenerate a coarse channel estimation. The interpolator matrix may beselected based on the speed of the device utilizing a Doppler estimate.The Doppler estimate may also be used in other parts of the system suchas the blocks 604 and second stage 2D-MMSE 610. The first stage channelestimation value (e.g., coarse channel estimation) may be used in blocky-h_hat SNR estimator 608 to generate an improved SNR estimation.

At the second stage, the improved SNR estimation may be used to selectanother 2D-MMSE interpolator matrix, which in turn may be used in secondstage 2D-MMSE 610 for channel estimation. The estimated channel may beused to estimate noise (N_(t)) in the N_(t) estimator 612. The estimatedchannel and noise values may then be fed to other parts of the system(e.g., physical downlink shared channel (PDSCH)) for further processing.The second stage of the channel estimation may be performed only if thedifference between the improved SNR estimate and the coarse SNR estimateis equal to or greater than a threshold. It should be noted that theblock y-h_hat SNR estimation 608 and the N_(t) estimator 612 process thepre-processed signal utilizing a channel estimation value. The secondstage 2D-MMSE 610 processes the pre-processed signal utilizing theimproved SNR estimate value.

FIG. 7 shows diagram 700 illustrating a variation of the two-stage SNRand channel estimation algorit_(h)m, in acco_(r)dance with certainaspects of the present disclosure. As illustrated, a predefined, fixedvalue (e.g., 20 dB or 25 dB) may be used as the raw SNR value in thefirst stage as illustrated in block Fixed SNR 614. Other blocks in FIG.7 are similar to the blocks illustrated in FIG. 6.

FIG. 8 shows diagram 800 illustrating throughput of a system utilizingthe 2-stage channel estimation algorithm. The system is assumed to havethe following characteristics: Typical Urban (TU) 120 kmh channel,bandwidth equal to 10 MHz, carrier frequency equal to 2 GHz, 2×2 Antennaconfiguration, adaptive modulation and coding, wideband CQI feedback,number of resource blocks (RBs) equal to 6, starting PRB index equal to6, perfect channel and estimated channel, perfect SNR, perfect estimatedSNR and quantized SNR, perfect N_(t) and estimated N_(t) and DRS pattern12A.

In plot 800, curve 802 illustrates the throughput for a system with thetwo step channel estimation. Curve 804 illustrates a system with perfectchannel knowledge. Curve 806 illustrates a system with perfect SNR.Curve 808 illustrates a system with threshold equal to 5 dB. Curve 810illustrates a system with two step channel estimation. And, curve 812illustrates a system with a fixed SNR equal to 20 dB and two stepchannel estimation.

FIG. 9 shows diagram 900 illustrating example operations for a channelestimation algorithm, in accordance with certain aspects of the presentdisclosure. At 902, a first signal to noise ratio (SNR) value may bedetermined for a received signal. For example, the first SNR may bedetermined by comparing a measured SNR value with a threshold value, andselecting a first value if the measured SNR value is equal to or greaterthan the threshold value and selecting a second value if the measuredSNR value is smaller than the threshold value.

At 904, based on the first SNR value, a first matrix may be selectedfrom one or more compressed interpolation matrices, wherein each of thecompressed interpolation matrices corresponds to a subset of symbols ina subframe. At 906, a first channel estimation matrix may be obtainedutilizing the received signal and the first matrix. At 908, a second SNRvalue may optionally be estimated based at least on the first channelestimation matrix. If a difference between the first and the second SNRvalues is equal to or greater than a threshold, at 910, a second matrixmay be selected from the compressed interpolation matrices based on thesecond SNR value. At 912, a second channel estimation matrix may beobtained utilizing the second matrix.

For certain aspects, the first channel estimation matrix may bedetermined by obtaining a first plurality of channel estimation valuescorresponding to the subset of symbols, and generating a secondplurality of channel estimation values, corresponding to the remainingsymbols in the subframe that are not included in the subset, byinterpolating the first plurality of channel estimation values. Thefirst channel estimation matrix may then be generated that may includethe first and the second plurality of channel estimation values.

The various operations of methods described above may be performed byany suitable means capable of performing the corresponding functions.The means may include various hardware and/or software component(s)and/or module(s), including, but not limited to a circuit, anapplication specific integrate circuit (ASIC), or processor. Generally,where there are operations illustrated in Figures, those operations mayhave corresponding counterpart means-plus-function components withsimilar numbering. For example, blocks 902-912 in FIG. 9 correspond tocircuit blocks 902A-912A illustrated in FIG. 9A.

For example, means for determining may comprise any suitable determiningcomponent, such as the SNR determining component 301 illustrated in FIG.3. Means for selecting may comprise any suitable selecting component,such as the interpolation matrix selecting component 303. Means forobtaining may comprise any suitable obtaining component, such as thechannel estimating component. These components may be implemented withany suitable components, such as one or more processors, for example,such as the RX data processor 270 m and/or controller 280 m of the userterminal 120 m, or the RX data processor 242 and/or controller 230 ofthe access point 110 illustrated in FIG. 2.

As used herein, the phrase “at least one of A or B” is meant to includeany combination of A and B. In other words, “at least one of A or B”comprises A or B or A and B.

As used herein, the term “determining” encompasses a wide variety ofactions. For example, “determining” may include calculating, computing,processing, deriving, investigating, looking up (e.g., looking up in atable, a database or another data structure), ascertaining and the like.Also, “determining” may include receiving (e.g., receiving information),accessing (e.g., accessing data in a memory) and the like. Also,“determining” may include resolving, selecting, choosing, establishingand the like.

The various operations of methods described above may be performed byany suitable means capable of performing the operations, such as varioushardware and/or software component(s), circuits, and/or module(s).Generally, any operations illustrated in the Figures may be performed bycorresponding functional means capable of performing the operations.

The various illustrative logical blocks, modules and circuits describedin connection with the present disclosure may be implemented orperformed with a general purpose processor, a digital signal processor(DSP), an application specific integrated circuit (ASIC), a fieldprogrammable gate array signal (FPGA) or other programmable logic device(PLD), discrete gate or transistor logic, discrete hardware componentsor any combination thereof designed to perform the functions describedherein. A general purpose processor may be a microprocessor, but in thealternative, the processor may be any commercially available processor,controller, microcontroller or state machine. A processor may also beimplemented as a combination of computing devices, e.g., a combinationof a DSP and a microprocessor, a plurality of microprocessors, one ormore microprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm described in connection with thepresent disclosure may be embodied directly in hardware, in a softwaremodule executed by a processor, or in a combination of the two. Asoftware module may reside in any form of storage medium that is knownin the art. Some examples of storage media that may be used includerandom access memory (RAM), read only memory (ROM), flash memory, EPROMmemory, EEPROM memory, registers, a hard disk, a removable disk, aCD-ROM and so forth. A software module may comprise a singleinstruction, or many instructions, and may be distributed over severaldifferent code segments, among different programs, and across multiplestorage media. A storage medium may be coupled to a processor such thatthe processor can read information from, and write information to, thestorage medium. In the alternative, the storage medium may be integralto the processor.

The methods disclosed herein comprise one or more steps or actions forachieving the described method. The method steps and/or actions may beinterchanged with one another without departing from the scope of theclaims. Unless a specific order of steps or actions is specified, theorder and/or use of specific steps and/or actions may be modifiedwithout departing from the scope of the claims.

The functions described may be implemented in hardware, software,firmware or any combination thereof. If implemented in software, thefunctions may be stored as one or more instructions on acomputer-readable medium. A storage media may be any available mediathat can be accessed by a computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code in the form of instructions or datastructures and that can be accessed by a computer. Disk and disc, asused herein, include compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and Blu-ray® disc where disksusually reproduce data magnetically, while discs reproduce dataoptically with lasers.

Thus, certain aspects may comprise a computer program product forperforming the operations presented herein. For example, such a computerprogram product may comprise a computer readable medium havinginstructions stored (and/or encoded) thereon, the instructions beingexecutable by one or more processors to perform the operations describedherein. For certain aspects, the computer program product may includepackaging material.

Software or instructions may also be transmitted over a transmissionmedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition oftransmission medium.

Further, it should be appreciated that modules and/or other appropriatemeans for performing the methods and techniques described herein can bedownloaded and/or otherwise obtained by a user terminal and/or basestation as applicable. For example, such a device can be coupled to aserver to facilitate the transfer of means for performing the methodsdescribed herein. Alternatively, various methods described herein can beprovided via storage means (e.g., RAM, ROM, a physical storage mediumsuch as a compact disc (CD) or floppy disk, etc.), such that a userterminal and/or base station can obtain the various methods uponcoupling or providing the storage means to the device. Moreover, anyother suitable technique for providing the methods and techniquesdescribed herein to a device can be utilized.

In one or more exemplary aspects, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored on ortransmitted over as one or more instructions or code on acomputer-readable medium. Computer-readable media includes both computerstorage media and communication media including any medium thatfacilitates transfer of a computer program from one place to another. Astorage media may be any available media that can be accessed by acomputer. By way of example, and not limitation, such computer-readablemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium that can be used to carry or store desired program code inthe form of instructions or data structures and that can be accessed bya computer. Also, any connection is properly termed a computer-readablemedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.Disk and disc, as used herein, includes compact disc (CD), laser disc,optical disc, digital versatile disc (DVD), floppy disk and Blu-ray discwhere disks usually reproduce data magnetically, while discs reproducedata optically with lasers. Thus, in some aspects computer readablemedium may comprise non-transitory computer readable medium (e.g.,tangible media). In addition, in some aspects computer readable mediummay comprise transitory computer readable medium (e.g., a signal).Combinations of the above should also be included within the scope ofcomputer-readable media.

Various functions described herein may be performed by a processingsystem. The processing system may be configured as a general-purposeprocessing system with one or more microprocessors providing theprocessor functionality and external memory providing at least a portionof the machine-readable media, all linked together with other supportingcircuitry through an external bus architecture. Alternatively, theprocessing system may be implemented with an ASIC (Application SpecificIntegrated Circuit) with the processor, the bus interface, the userinterface in the case of an access terminal), supporting circuitry (notshown), and at least a portion of the machine-readable media integratedinto a single chip, or with one or more FPGAs (Field Programmable GateArray), PLDs (Programmable Logic Device), controllers, state machines,gated logic, discrete hardware components, or any other suitablecircuitry, or any combination of circuits that can perform the variousfunctionality described throughout this disclosure. Those skilled in theart will recognize how best to implement the described functionality forthe processing system depending on the particular application and theoverall design constraints imposed on the overall system.

It is to be understood that the claims are not limited to the preciseconfiguration and components illustrated above. Various modifications,changes and variations may be made in the arrangement, operation anddetails of the methods and apparatus described above without departingfrom the scope of the claims.

The techniques provided herein may be utilized in a variety ofapplications. For certain aspects, the techniques presented herein maybe incorporated in an access point, an access terminal, a mobilehandset, or other type of wireless device with processing logic andelements to perform the techniques provided herein.

While the foregoing is directed to aspects of the present invention,other and further aspects of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

What is claimed is:
 1. A method for wireless communications, comprising:determining a first signal to noise ratio (SNR) value for a receivedsignal; selecting, based on the first SNR value, a first matrix from oneor more two-dimensional compressed interpolation matrices, wherein eachof the compressed interpolation matrices corresponds to a subset ofphysical resource blocks (PRBs) in a subframe, and wherein the size ofthe compressed interpolation matrices is based on a number of dedicatedreference signal (DRS) tones in the subframe and a number of symbols inthe subset of PRBs; and obtaining a first channel estimation matrixutilizing the received signal and the first matrix.
 2. The method ofclaim 1, wherein obtaining the first channel estimation matrixcomprises: obtaining a first plurality of channel estimation valuescorresponding to the subset of physical resource blocks (PRBs); andgenerating a second plurality of channel estimation values,corresponding to remaining PRBs in the subframe that are not included inthe subset of PRBs, by interpolating the first plurality of channelestimation values; and generating the first channel estimation matrixcomprising the first and the second plurality of channel estimationvalues.
 3. The method of claim 1, further comprising: estimating asecond SNR value based at least on the first channel estimation matrix;selecting a second matrix from the compressed interpolation matricesbased on the second SNR value; and obtaining a second channel estimationmatrix utilizing the second matrix.
 4. The method of claim 3, furthercomprising: comparing the first and the second SNR values and selectingthe second matrix only if a difference between the first SNR value andthe second SNR value is equal to or greater than a threshold value. 5.The method of claim 1, wherein obtaining the first channel estimationmatrix comprises: obtaining the first channel estimation matrixutilizing a two-dimensional minimum mean square error (2D-MMSE)algorithm.
 6. The method of claim 1, wherein determining the first SNRvalue comprises: determining the first SNR value utilizing one or morecommon reference signals.
 7. The method of claim 1, wherein determiningthe first SNR value comprises: comparing a measured SNR value with athreshold value; and selecting, for the first SNR value, a first valueif the measured SNR value is equal to or greater than the thresholdvalue and selecting a second value if the measured SNR value is smallerthan the threshold value.
 8. The method of claim 1, wherein determiningthe first SNR value comprises: utilizing a predefined value for thefirst SNR value.
 9. The method of claim 1, further comprising: obtaininga Doppler value; and selecting the first matrix further based at leaston the Doppler value.
 10. The method of claim 1, wherein determining thefirst SNR value comprises: determining the first SNR value based onmapping of a modulation and coding scheme.
 11. An apparatus for wirelesscommunications, comprising: means for determining a first signal tonoise ratio (SNR) value for a received signal; means for selecting,based on the first SNR value, a first matrix from one or moretwo-dimensional compressed interpolation matrices, wherein each of thecompressed interpolation matrices corresponds to a subset of physicalresource blocks (PRBs) in a subframe, and wherein the size of thecompressed interpolation matrices is based on a number of dedicatedreference signal (DRS) tones in the subframe and a number of symbols inthe subset of PRBs; and means for obtaining a first channel estimationmatrix utilizing the received signal and the first matrix.
 12. Theapparatus of claim 11, wherein the means for obtaining the first channelestimation matrix comprises: means for obtaining a first plurality ofchannel estimation values corresponding to the subset of physicalresource blocks (PRBs); and means for generating a second plurality ofchannel estimation values, corresponding to remaining PRBs in thesubframe that are not included in the subset of PRBs, by interpolatingthe first plurality of channel estimation values; and means forgenerating the first channel estimation matrix comprising the first andthe second plurality of channel estimation values.
 13. The apparatus ofclaim 11, further comprising: means for estimating a second SNR valuebased at least on the first channel estimation matrix; means forselecting a second matrix from the compressed interpolation matricesbased on the second SNR value; and means for obtaining a second channelestimation matrix utilizing the second matrix.
 14. The apparatus ofclaim 13, further comprising: means for comparing the first and thesecond SNR values and selecting the second matrix only if a differencebetween the first SNR value and the second SNR value is equal to orgreater than a threshold value.
 15. The apparatus of claim 11, whereinthe means for obtaining the first channel estimation matrix comprises:means for obtaining the first channel estimation matrix utilizing atwo-dimensional minimum mean square error (2D-MMSE) algorithm.
 16. Theapparatus of claim 11, wherein the means for determining the first SNRvalue comprises: means for determining the first SNR value utilizing oneor more common reference signals.
 17. The apparatus of claim 11, whereinthe means for determining the first SNR value comprises: means forcomparing a measured SNR value with a threshold value; and means forselecting, for the first SNR value, a first value if the measured SNRvalue is equal to or greater than the threshold value and selecting asecond value if the measured SNR value is smaller than the thresholdvalue.
 18. The apparatus of claim 11, wherein the means for determiningthe first SNR value comprises: means for utilizing a predefined valuefor the first SNR value.
 19. The apparatus of claim 11, furthercomprising: means for obtaining a Doppler value; and means for selectingthe first matrix further based at least on the Doppler value.
 20. Theapparatus of claim 11, wherein the means for determining the first SNRvalue comprises: means for determining the first SNR value based onmapping of a modulation and coding scheme.
 21. An apparatus for wirelesscommunications, comprising: logic for determining a first signal tonoise ratio (SNR) value for a received signal; logic for selecting,based on the first SNR value, a first matrix from one or moretwo-dimensional compressed interpolation matrices, wherein each of thecompressed interpolation matrices corresponds to a subset of physicalresource blocks (PRBs) in a subframe, and wherein the size of thecompressed interpolation matrices is based on a number of dedicatedreference signal (DRS) tones in the subframe and a number of symbols inthe subset of PRBs; and logic for obtaining a first channel estimationmatrix utilizing the received signal and the first matrix.
 22. Theapparatus of claim 21, wherein the logic for obtaining the first channelestimation matrix comprises: logic for obtaining a first plurality ofchannel estimation values corresponding to the subset of physicalresource blocks (PRBs); and logic for generating a second plurality ofchannel estimation values, corresponding to remaining PRBs in thesubframe that are not included in the subset of PRBs, by interpolatingthe first plurality of channel estimation values; and logic forgenerating the first channel estimation matrix comprising the first andthe second plurality of channel estimation values.
 23. The apparatus ofclaim 21, further comprising: logic for estimating a second SNR valuebased at least on the first channel estimation matrix; logic forselecting a second matrix from the compressed interpolation matricesbased on the second SNR value; and logic for obtaining a second channelestimation matrix utilizing the second matrix.
 24. The apparatus ofclaim 23, further comprising: logic for comparing the first and thesecond SNR values and selecting the second matrix only if a differencebetween the first SNR value and the second SNR value is equal to orgreater than a threshold value.
 25. The apparatus of claim 21, whereinthe logic for obtaining the first channel estimation matrix comprises:logic for obtaining the first channel estimation matrix utilizing atwo-dimensional minimum mean square error (2D-MMSE) algorithm.
 26. Theapparatus of claim 21, wherein the logic for determining the first SNRvalue comprises: logic for determining the first SNR value utilizing oneor more common reference signals.
 27. The apparatus of claim 21, whereinthe logic for determining the first SNR value comprises: logic forcomparing a measured SNR value with a threshold value; and logic forselecting, for the first SNR value, a first value if the measured SNRvalue is equal to or greater than the threshold value and selecting asecond value if the measured SNR value is smaller than the thresholdvalue.
 28. The apparatus of claim 21, wherein the logic for determiningthe first SNR value comprises: logic for utilizing a predefined valuefor the first SNR value.
 29. The apparatus of claim 21, furthercomprising: logic for obtaining a Doppler value; and logic for selectingthe first matrix further based at least on the Doppler value.
 30. Theapparatus of claim 21, wherein the logic for determining the first SNRvalue comprises: logic for determining the first SNR value based onmapping of a modulation and coding scheme.
 31. An apparatus for wirelesscommunications, comprising at least one processor configured to:determine a first signal to noise ratio (SNR) value for a receivedsignal, select, based on the first SNR value, a first matrix from one ormore two-dimensional compressed interpolation matrices, wherein each ofthe compressed interpolation matrices corresponds to a subset ofphysical resource blocks (PRBs) in a subframe, and wherein the size ofthe compressed interpolation matrices is based on a number of dedicatedreference signal (DRS) tones in the subframe and a number of symbols inthe subset of PRBs, and obtain a first channel estimation matrixutilizing the received signal and the first matrix; and a memory coupledto the at least one processor.
 32. The apparatus of claim 31, whereinthe processor is configured to obtain the first channel estimationmatrix by: obtaining a first plurality of channel estimation valuescorresponding to the subset of physical resource blocks (PRBs); andgenerating a second plurality of channel estimation values,corresponding to remaining PRBs in the subframe that are not included inthe subset of PRBs, by interpolating the first plurality of channelestimation values; and generating the first channel estimation matrixcomprising the first and the second plurality of channel estimationvalues.
 33. The apparatus of claim 31, wherein the processor is furtherconfigured to: estimate a second SNR value based at least on the firstchannel estimation matrix; select a second matrix from the compressedinterpolation matrices based on the second SNR value; and obtain asecond channel estimation matrix utilizing the second matrix.
 34. Theapparatus of claim 33, wherein the processor is further configured to:compare the first and the second SNR values and selecting the secondmatrix only if a difference between the first SNR value and the secondSNR value is equal to or greater than a threshold value.
 35. Theapparatus of claim 31, wherein the processor is configured to obtain thefirst channel estimation matrix by: obtaining the first channelestimation matrix utilizing a two-dimensional minimum mean square error(2D-MMSE) algorithm.
 36. The apparatus of claim 31, wherein theprocessor is configured to determine the first SNR value by: determiningthe first SNR value utilizing one or more common reference signals. 37.The apparatus of claim 31, wherein the processor is configured todetermine the first SNR value by: comparing a measured SNR value with athreshold value; and selecting, for the first SNR value, a first valueif the measured SNR value is equal to or greater than the thresholdvalue and selecting a second value if the measured SNR value is smallerthan the threshold value.
 38. The apparatus of claim 31, wherein theprocessor is configured to determine the first SNR value by: utilizing apredefined value for the first SNR value.
 39. The apparatus of claim 31,wherein the processor is further configured to: obtain a Doppler value;and select the first matrix further based at least on the Doppler value.40. The apparatus of claim 31, wherein the processor is configured todetermine the first SNR value by: determining the first SNR value basedon mapping of a modulation and coding scheme.
 41. A computer-programproduct for wireless communications, comprising a non-transitorycomputer readable medium having instructions stored thereon, theinstructions being executable by one or more processors and theinstructions comprising: instructions for determining a first signal tonoise ratio (SNR) value for a received signal; instructions forselecting, based on the first SNR value, a first matrix from one or moretwo-dimensional compressed interpolation matrices, wherein each of thecompressed interpolation matrices corresponds to a subset of physicalresource blocks (PRBs) in a subframe, and wherein the size of thecompressed interpolation matrices is based on a number of dedicatedreference signal (DRS) tones in the subframe and a number of symbols inthe subset of PRBs; and instructions for obtaining a first channelestimation matrix utilizing the received signal and the first matrix.